Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Goes beyond the annotations by revealing the algorithmic limitation (surface-level word matching) with a concrete example. It also adds traits such as 'fast' and 'deterministic', which are not present in the annotations but are important behavioral context. No contradictions with annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is extremely concise, using only two sentences to convey purpose, method, limitations, and alternatives. Every sentence adds value, and the text is well-structured with a clear separation of strengths, limitations, and use case.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The tool is simple, has full parameter documentation, and an output schema. The description covers use cases, limitations, and alternatives, making it complete for an agent to decide when to use it. There are no obvious gaps given the available structured information.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema already provides 100% coverage for both parameters, so the baseline is 3. The description adds slight context by explaining that check_facts deals with 'fact drift (number comparison)', but this is already implied by the schema description. No additional parameter-level detail is provided in the description.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: compare multiple LLM responses to the same prompt and detect inconsistencies via Jaccard similarity and fact drift. It names specific methods and distinguishes from sibling tools like run_semantic_tests by explicitly referencing embedding mode for semantic consistency.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides clear when-to-use context: 'Essential for determinism testing' and highlights the fast/deterministic/no-API-key benefits. It explicitly states when not to rely on it (semantic equivalence) and directs to run_semantic_tests as an alternative for true semantic consistency.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.